merge upstream

.github/workflows/windows_release_dependencies.yml

@@ -41,13 +41,13 @@ jobs:
         - shell: bash
           run: |
             echo "@echo off
-            ..\python_embeded\python.exe .\update.py ..\ComfyUI\
+            ..\python_embeded\python.exe .\update.py ..\ComfyUI\\
-            echo
+            echo -
             echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff
             echo You should not be running this anyways unless you really have to
-            echo
+            echo -
             echo If you just want to update normally, close this and run update_comfyui.bat instead.
-            echo
+            echo -
             pause
             ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
             pause" > update_comfyui_and_python_dependencies.bat

README.md

@@ -46,6 +46,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 | Ctrl + S                  | Save workflow                                                                                                      |
 | Ctrl + O                  | Load workflow                                                                                                      |
 | Ctrl + A                  | Select all nodes                                                                                                   |
+| Alt + C                   | Collapse/uncollapse selected nodes                                                                                 |
 | Ctrl + M                  | Mute/unmute selected nodes                                                                                         |
 | Ctrl + B                  | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
 | Delete/Backspace          | Delete selected nodes                                                                                              |
@@ -93,7 +94,7 @@ Ctrl can also be replaced with Cmd instead for macOS users
 
 There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
 
-### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z)
+### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_cu121_or_cpu.7z)
 
 Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
 
@@ -127,6 +128,8 @@ On macOS, install exactly Python 3.11 using `brew`, which you can download from
    source ./venv/bin/activate
    ```
 
+Note: pytorch does not support python 3.12 yet so make sure your python version is 3.11 or earlier.
+
 3. Then, run the following command to install `comfyui` into your current environment. This will correctly select the version of pytorch that matches the GPU on your machine (NVIDIA or CPU on Windows, NVIDIA AMD or CPU on Linux):
     ```shell
     pip install git+https://github.com/hiddenswitch/ComfyUI.git

comfy/cldm/cldm.py

@@ -34,8 +34,7 @@ class ControlNet(nn.Module):
         dims=2,
         num_classes=None,
         use_checkpoint=False,
-        use_fp16=False,
+        dtype=torch.float32,
-        use_bf16=False,
         num_heads=-1,
         num_head_channels=-1,
         num_heads_upsample=-1,
@@ -108,8 +107,7 @@ class ControlNet(nn.Module):
         self.conv_resample = conv_resample
         self.num_classes = num_classes
         self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
+        self.dtype = dtype
-        self.dtype = th.bfloat16 if use_bf16 else self.dtype
         self.num_heads = num_heads
         self.num_head_channels = num_head_channels
         self.num_heads_upsample = num_heads_upsample

comfy/cli_args.py

@@ -39,6 +39,7 @@ parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORI
 parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
 parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
 parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
+parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.")
 parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
 parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
 parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
@@ -52,6 +53,8 @@ fp_group = parser.add_mutually_exclusive_group()
 fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
 fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
 
+parser.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
+
 fpvae_group = parser.add_mutually_exclusive_group()
 fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")
 fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.")

comfy/cmd/execution.py

@@ -4,6 +4,8 @@ import asyncio
 import copy
 import datetime
 import heapq
+import json
+import logging
 import threading
 import time
 import traceback
@@ -16,7 +18,7 @@ import torch
 
 from ..nodes.package import import_all_nodes_in_workspace
 nodes = import_all_nodes_in_workspace()
-from .. import model_management
+from .. import model_management  # type: ignore
 
 """
 A queued item
@@ -209,7 +211,7 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
                 server.send_sync("executed", {"node": unique_id, "output": output_ui, "prompt_id": prompt_id},
                              server.client_id)
     except model_management.InterruptProcessingException as iex:
-        print("Processing interrupted")
+        logging.info("Processing interrupted")
 
         # skip formatting inputs/outputs
         error_details = {
@@ -230,8 +232,8 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
         for node_id, node_outputs in outputs.items():
             output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]
 
-        print("!!! Exception during processing !!!")
+        logging.error("!!! Exception during processing !!!")
-        print(traceback.format_exc())
+        logging.error(traceback.format_exc())
 
         error_details = {
             "node_id": unique_id,
@@ -443,7 +445,7 @@ class PromptExecutor:
 
 
 
-def validate_inputs(prompt, item, validated) -> Tuple[bool, str, typing.Any]:
+def validate_inputs(prompt, item, validated) -> Tuple[bool, typing.List[dict], typing.Any]:
     # todo: this should check if LoadImage / LoadImageMask paths exist
     # todo: or, nodes should provide a way to validate their values
     unique_id = item
@@ -511,8 +513,8 @@ def validate_inputs(prompt, item, validated) -> Tuple[bool, str, typing.Any]:
                 errors.append(error)
                 continue
             try:
-                r = validate_inputs(prompt, o_id, validated)
+                r2 = validate_inputs(prompt, o_id, validated)
-                if r[0] is False:
+                if r2[0] is False:
                     # `r` will be set in `validated[o_id]` already
                     valid = False
                     continue
@@ -593,11 +595,11 @@ def validate_inputs(prompt, item, validated) -> Tuple[bool, str, typing.Any]:
                 input_data_all = get_input_data(inputs, obj_class, unique_id)
                 # ret = obj_class.VALIDATE_INPUTS(**input_data_all)
                 ret = map_node_over_list(obj_class, input_data_all, "VALIDATE_INPUTS")
-                for i, r in enumerate(ret):
+                for i, r3 in enumerate(ret):
-                    if r is not True:
+                    if r3 is not True:
                         details = f"{x}"
-                        if r is not False:
+                        if r3 is not False:
-                            details += f" - {str(r)}"
+                            details += f" - {str(r3)}"
 
                         error = {
                             "type": "custom_validation_failed",
@@ -698,11 +700,11 @@ def validate_prompt(prompt: dict) -> typing.Tuple[bool, dict | typing.List[dict]
         if valid is True:
             good_outputs.add(o)
         else:
-            print(f"Failed to validate prompt for output {o}:")
+            logging.error(f"Failed to validate prompt for output {o}:")
             if len(reasons) > 0:
-                print("* (prompt):")
+                logging.error("* (prompt):")
                 for reason in reasons:
-                    print(f"  - {reason['message']}: {reason['details']}")
+                    logging.error(f"  - {reason['message']}: {reason['details']}")
             errors += [(o, reasons)]
             for node_id, result in validated.items():
                 valid = result[0]
@@ -718,16 +720,16 @@ def validate_prompt(prompt: dict) -> typing.Tuple[bool, dict | typing.List[dict]
                             "dependent_outputs": [],
                             "class_type": class_type
                         }
-                        print(f"* {class_type} {node_id}:")
+                        logging.error(f"* {class_type} {node_id}:")
                         for reason in reasons:
-                            print(f"  - {reason['message']}: {reason['details']}")
+                            logging.error(f"  - {reason['message']}: {reason['details']}")
                     node_errors[node_id]["dependent_outputs"].append(o)
-            print("Output will be ignored")
+            logging.error("Output will be ignored")
 
     if len(good_outputs) == 0:
         errors_list = []
-        for o, errors in errors:
+        for o, _errors in errors:
-            for error in errors:
+            for error in _errors:
                 errors_list.append(f"{error['message']}: {error['details']}")
         errors_list = "\n".join(errors_list)
 

comfy/cmd/folder_paths.py

@@ -33,6 +33,8 @@ folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes
 
 folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
 
+folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
+
 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")
 input_directory = os.path.join(base_path, "input")
@@ -50,6 +52,10 @@ def set_temp_directory(temp_dir):
     global temp_directory
     temp_directory = temp_dir
 
+def set_input_directory(input_dir):
+    global input_directory
+    input_directory = input_dir
+
 def get_output_directory():
     global output_directory
     return output_directory
@@ -144,7 +150,7 @@ def recursive_search(directory, excluded_dir_names=None):
     return result, dirs
 
 def filter_files_extensions(files, extensions):
-    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))
+    return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
 
 
 

comfy/cmd/latent_preview.py

@@ -55,7 +55,12 @@ def get_previewer(device, latent_format):
         # TODO previewer methods
         taesd_decoder_path = None
         if latent_format.taesd_decoder_name is not None:
-            taesd_decoder_path = folder_paths.get_full_path("vae_approx", latent_format.taesd_decoder_name)
+            taesd_decoder_path = next(
+                (fn for fn in folder_paths.get_filename_list("vae_approx")
+                    if fn.startswith(latent_format.taesd_decoder_name)),
+                ""
+            )
+            taesd_decoder_path = folder_paths.get_full_path("vae_approx", taesd_decoder_path)
 
         if method == LatentPreviewMethod.Auto:
             method = LatentPreviewMethod.Latent2RGB

comfy/cmd/main.py

@@ -178,6 +178,16 @@ def main():
         print(f"Setting output directory to: {output_dir}")
         folder_paths.set_output_directory(output_dir)
 
+    #These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes
+    folder_paths.add_model_folder_path("checkpoints", os.path.join(folder_paths.get_output_directory(), "checkpoints"))
+    folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip"))
+    folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae"))
+
+    if args.input_directory:
+        input_dir = os.path.abspath(args.input_directory)
+        print(f"Setting input directory to: {input_dir}")
+        folder_paths.set_input_directory(input_dir)
+
     if args.quick_test_for_ci:
         exit(0)
 

comfy/cmd/server.py

@@ -6,6 +6,7 @@ import struct
 import sys
 import shutil
 from urllib.parse import quote
+from pkg_resources import resource_filename
 
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
@@ -105,7 +106,6 @@ class PromptServer():
         self.sockets = dict()
         web_root_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../web")
         if not os.path.exists(web_root_path):
-            from pkg_resources import resource_filename
             web_root_path = resource_filename('comfy', 'web/')
         self.web_root = web_root_path
         routes = web.RouteTableDef()

comfy/controlnet.py

@@ -292,8 +292,8 @@ def load_controlnet(ckpt_path, model=None):
 
     controlnet_config = None
     if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
-        use_fp16 = comfy.model_management.should_use_fp16()
+        unet_dtype = comfy.model_management.unet_dtype()
-        controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16)
+        controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype)
         diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
         diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
         diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
@@ -353,8 +353,8 @@ def load_controlnet(ckpt_path, model=None):
         return net
 
     if controlnet_config is None:
-        use_fp16 = comfy.model_management.should_use_fp16()
+        unet_dtype = comfy.model_management.unet_dtype()
-        controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16, True).unet_config
+        controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config
     controlnet_config.pop("out_channels")
     controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
     control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
@@ -383,8 +383,7 @@ def load_controlnet(ckpt_path, model=None):
         missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
     print(missing, unexpected)
 
-    if use_fp16:
+    control_model = control_model.to(unet_dtype)
-        control_model = control_model.half()
 
     global_average_pooling = False
     filename = os.path.splitext(ckpt_path)[0]
@@ -456,7 +455,7 @@ def load_t2i_adapter(t2i_data):
         for i in range(4):
             for j in range(2):
                 prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j)
-            prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2)
+            prefix_replace["adapter.body.{}.".format(i)] = "body.{}.".format(i * 2)
         prefix_replace["adapter."] = ""
         t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace)
     keys = t2i_data.keys()

comfy/for_developers/lib_comfy.py

@@ -1,13 +0,0 @@
-from asyncio import AbstractEventLoop
-
-from ..cmd.execution import PromptQueue
-from ..cmd.server import PromptServer
-
-
-class Comfy:
-    loop: AbstractEventLoop
-    server: PromptServer
-    queue: PromptQueue
-
-    def __init__(self):
-        pass

comfy/latent_formats.py

@@ -20,7 +20,7 @@ class SD15(LatentFormat):
                     [-0.2829,  0.1762,  0.2721],
                     [-0.2120, -0.2616, -0.7177]
                 ]
-        self.taesd_decoder_name = "taesd_decoder.pth"
+        self.taesd_decoder_name = "taesd_decoder"
 
 class SDXL(LatentFormat):
     def __init__(self):
@@ -32,4 +32,4 @@ class SDXL(LatentFormat):
                     [ 0.0568,  0.1687, -0.0755],
                     [-0.3112, -0.2359, -0.2076]
                 ]
-        self.taesd_decoder_name = "taesdxl_decoder.pth"
+        self.taesd_decoder_name = "taesdxl_decoder"

comfy/ldm/modules/attention.py

@@ -93,253 +93,222 @@ def zero_module(module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
+def attention_basic(q, k, v, heads, mask=None):
+    h = heads
+    scale = (q.shape[-1] // heads) ** -0.5
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
-class SpatialSelfAttention(nn.Module):
+    # force cast to fp32 to avoid overflowing
-    def __init__(self, in_channels):
+    if _ATTN_PRECISION =="fp32":
-        super().__init__()
+        with torch.autocast(enabled=False, device_type = 'cuda'):
-        self.in_channels = in_channels
+            q, k = q.float(), k.float()
+            sim = einsum('b i d, b j d -> b i j', q, k) * scale
+    else:
+        sim = einsum('b i d, b j d -> b i j', q, k) * scale
 
-        self.norm = Normalize(in_channels)
+    del q, k
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
 
-    def forward(self, x):
+    if exists(mask):
-        h_ = x
+        mask = rearrange(mask, 'b ... -> b (...)')
-        h_ = self.norm(h_)
+        max_neg_value = -torch.finfo(sim.dtype).max
-        q = self.q(h_)
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
-        k = self.k(h_)
+        sim.masked_fill_(~mask, max_neg_value)
-        v = self.v(h_)
 
-        # compute attention
+    # attention, what we cannot get enough of
-        b,c,h,w = q.shape
+    sim = sim.softmax(dim=-1)
-        q = rearrange(q, 'b c h w -> b (h w) c')
-        k = rearrange(k, 'b c h w -> b c (h w)')
-        w_ = torch.einsum('bij,bjk->bik', q, k)
 
-        w_ = w_ * (int(c)**(-0.5))
+    out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
-        w_ = torch.nn.functional.softmax(w_, dim=2)
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-
+    return out
-        # attend to values
-        v = rearrange(v, 'b c h w -> b c (h w)')
-        w_ = rearrange(w_, 'b i j -> b j i')
-        h_ = torch.einsum('bij,bjk->bik', v, w_)
-        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
-        h_ = self.proj_out(h_)
-
-        return x+h_
 
 
-class CrossAttentionBirchSan(nn.Module):
+def attention_sub_quad(query, key, value, heads, mask=None):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops):
+    scale = (query.shape[-1] // heads) ** -0.5
-        super().__init__()
+    query = query.unflatten(-1, (heads, -1)).transpose(1,2).flatten(end_dim=1)
-        inner_dim = dim_head * heads
+    key_t = key.transpose(1,2).unflatten(1, (heads, -1)).flatten(end_dim=1)
-        context_dim = default(context_dim, query_dim)
+    del key
+    value = value.unflatten(-1, (heads, -1)).transpose(1,2).flatten(end_dim=1)
 
-        self.scale = dim_head ** -0.5
+    dtype = query.dtype
-        self.heads = heads
+    upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
+    if upcast_attention:
+        bytes_per_token = torch.finfo(torch.float32).bits//8
+    else:
+        bytes_per_token = torch.finfo(query.dtype).bits//8
+    batch_x_heads, q_tokens, _ = query.shape
+    _, _, k_tokens = key_t.shape
+    qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
 
-        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+    mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
-        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
 
-        self.to_out = nn.Sequential(
+    chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
-            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
-            nn.Dropout(dropout)
-        )
 
-    def forward(self, x, context=None, value=None, mask=None):
+    kv_chunk_size_min = None
-        h = self.heads
 
-        query = self.to_q(x)
+    #not sure at all about the math here
-        context = default(context, x)
+    #TODO: tweak this
-        key = self.to_k(context)
+    if mem_free_total > 8192 * 1024 * 1024 * 1.3:
-        if value is not None:
+        query_chunk_size_x = 1024 * 4
-            value = self.to_v(value)
+    elif mem_free_total > 4096 * 1024 * 1024 * 1.3:
-        else:
+        query_chunk_size_x = 1024 * 2
-            value = self.to_v(context)
+    else:
+        query_chunk_size_x = 1024
+    kv_chunk_size_min_x = None
+    kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
+    if kv_chunk_size_x < 1024:
+        kv_chunk_size_x = None
 
-        del context, x
+    if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
+        # the big matmul fits into our memory limit; do everything in 1 chunk,
+        # i.e. send it down the unchunked fast-path
+        query_chunk_size = q_tokens
+        kv_chunk_size = k_tokens
+    else:
+        query_chunk_size = query_chunk_size_x
+        kv_chunk_size = kv_chunk_size_x
+        kv_chunk_size_min = kv_chunk_size_min_x
 
-        query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
+    hidden_states = efficient_dot_product_attention(
-        key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1)
+        query,
-        del key
+        key_t,
-        value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
+        value,
+        query_chunk_size=query_chunk_size,
+        kv_chunk_size=kv_chunk_size,
+        kv_chunk_size_min=kv_chunk_size_min,
+        use_checkpoint=False,
+        upcast_attention=upcast_attention,
+    )
 
-        dtype = query.dtype
+    hidden_states = hidden_states.to(dtype)
-        upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
-        if upcast_attention:
-            bytes_per_token = torch.finfo(torch.float32).bits//8
-        else:
-            bytes_per_token = torch.finfo(query.dtype).bits//8
-        batch_x_heads, q_tokens, _ = query.shape
-        _, _, k_tokens = key_t.shape
-        qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
 
-        mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
+    hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
+    return hidden_states
 
-        chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
+def attention_split(q, k, v, heads, mask=None):
+    scale = (q.shape[-1] // heads) ** -0.5
+    h = heads
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
-        kv_chunk_size_min = None
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
 
-        #not sure at all about the math here
+    mem_free_total = model_management.get_free_memory(q.device)
-        #TODO: tweak this
-        if mem_free_total > 8192 * 1024 * 1024 * 1.3:
-            query_chunk_size_x = 1024 * 4
-        elif mem_free_total > 4096 * 1024 * 1024 * 1.3:
-            query_chunk_size_x = 1024 * 2
-        else:
-            query_chunk_size_x = 1024
-        kv_chunk_size_min_x = None
-        kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
-        if kv_chunk_size_x < 1024:
-            kv_chunk_size_x = None
 
-        if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
+    gb = 1024 ** 3
-            # the big matmul fits into our memory limit; do everything in 1 chunk,
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
-            # i.e. send it down the unchunked fast-path
+    modifier = 3 if q.element_size() == 2 else 2.5
-            query_chunk_size = q_tokens
+    mem_required = tensor_size * modifier
-            kv_chunk_size = k_tokens
+    steps = 1
-        else:
-            query_chunk_size = query_chunk_size_x
-            kv_chunk_size = kv_chunk_size_x
-            kv_chunk_size_min = kv_chunk_size_min_x
-
-        hidden_states = efficient_dot_product_attention(
-            query,
-            key_t,
-            value,
-            query_chunk_size=query_chunk_size,
-            kv_chunk_size=kv_chunk_size,
-            kv_chunk_size_min=kv_chunk_size_min,
-            use_checkpoint=self.training,
-            upcast_attention=upcast_attention,
-        )
-
-        hidden_states = hidden_states.to(dtype)
-
-        hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2)
-
-        out_proj, dropout = self.to_out
-        hidden_states = out_proj(hidden_states)
-        hidden_states = dropout(hidden_states)
-
-        return hidden_states
 
 
-class CrossAttentionDoggettx(nn.Module):
+    if mem_required > mem_free_total:
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops):
+        steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-        super().__init__()
+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
-        inner_dim = dim_head * heads
+        #      f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-        context_dim = default(context_dim, query_dim)
 
-        self.scale = dim_head ** -0.5
+    if steps > 64:
-        self.heads = heads
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+                            f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
 
-        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+    # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
-        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+    first_op_done = False
-        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+    cleared_cache = False
-
+    while True:
-        self.to_out = nn.Sequential(
+        try:
-            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
+            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-            nn.Dropout(dropout)
+            for i in range(0, q.shape[1], slice_size):
-        )
+                end = i + slice_size
-
+                if _ATTN_PRECISION =="fp32":
-    def forward(self, x, context=None, value=None, mask=None):
+                    with torch.autocast(enabled=False, device_type = 'cuda'):
-        h = self.heads
+                        s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
-
-        q_in = self.to_q(x)
-        context = default(context, x)
-        k_in = self.to_k(context)
-        if value is not None:
-            v_in = self.to_v(value)
-            del value
-        else:
-            v_in = self.to_v(context)
-        del context, x
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
-        del q_in, k_in, v_in
-
-        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-
-        mem_free_total = model_management.get_free_memory(q.device)
-
-        gb = 1024 ** 3
-        tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
-        modifier = 3 if q.element_size() == 2 else 2.5
-        mem_required = tensor_size * modifier
-        steps = 1
-
-
-        if mem_required > mem_free_total:
-            steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
-            # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
-            #      f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-
-        if steps > 64:
-            max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
-            raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
-                               f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
-
-        # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
-        first_op_done = False
-        cleared_cache = False
-        while True:
-            try:
-                slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-                for i in range(0, q.shape[1], slice_size):
-                    end = i + slice_size
-                    if _ATTN_PRECISION =="fp32":
-                        with torch.autocast(enabled=False, device_type = 'cuda'):
-                            s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale
-                    else:
-                        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
-                    first_op_done = True
-
-                    s2 = s1.softmax(dim=-1).to(v.dtype)
-                    del s1
-
-                    r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
-                    del s2
-                break
-            except model_management.OOM_EXCEPTION as e:
-                if first_op_done == False:
-                    model_management.soft_empty_cache(True)
-                    if cleared_cache == False:
-                        cleared_cache = True
-                        print("out of memory error, emptying cache and trying again")
-                        continue
-                    steps *= 2
-                    if steps > 64:
-                        raise e
-                    print("out of memory error, increasing steps and trying again", steps)
                 else:
+                    s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
+                first_op_done = True
+
+                s2 = s1.softmax(dim=-1).to(v.dtype)
+                del s1
+
+                r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+                del s2
+            break
+        except model_management.OOM_EXCEPTION as e:
+            if first_op_done == False:
+                model_management.soft_empty_cache(True)
+                if cleared_cache == False:
+                    cleared_cache = True
+                    print("out of memory error, emptying cache and trying again")
+                    continue
+                steps *= 2
+                if steps > 64:
                     raise e
+                print("out of memory error, increasing steps and trying again", steps)
+            else:
+                raise e
 
-        del q, k, v
+    del q, k, v
 
-        r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
-        del r1
+    del r1
+    return r2
 
-        return self.to_out(r2)
+def attention_xformers(q, k, v, heads, mask=None):
+    b, _, _ = q.shape
+    q, k, v = map(
+        lambda t: t.unsqueeze(3)
+        .reshape(b, t.shape[1], heads, -1)
+        .permute(0, 2, 1, 3)
+        .reshape(b * heads, t.shape[1], -1)
+        .contiguous(),
+        (q, k, v),
+    )
+
+    # actually compute the attention, what we cannot get enough of
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
+
+    if exists(mask):
+        raise NotImplementedError
+    out = (
+        out.unsqueeze(0)
+        .reshape(b, heads, out.shape[1], -1)
+        .permute(0, 2, 1, 3)
+        .reshape(b, out.shape[1], -1)
+    )
+    return out
+
+def attention_pytorch(q, k, v, heads, mask=None):
+    b, _, dim_head = q.shape
+    dim_head //= heads
+    q, k, v = map(
+        lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+        (q, k, v),
+    )
+
+    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+    out = (
+        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+    )
+    return out
+
+
+optimized_attention = attention_basic
+optimized_attention_masked = attention_basic
+
+if model_management.xformers_enabled():
+    print("Using xformers cross attention")
+    optimized_attention = attention_xformers
+elif model_management.pytorch_attention_enabled():
+    print("Using pytorch cross attention")
+    optimized_attention = attention_pytorch
+else:
+    if args.use_split_cross_attention:
+        print("Using split optimization for cross attention")
+        optimized_attention = attention_split
+    else:
+        print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
+        optimized_attention = attention_sub_quad
+
+if model_management.pytorch_attention_enabled():
+    optimized_attention_masked = attention_pytorch
 
 class CrossAttention(nn.Module):
     def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops):
@@ -347,62 +316,6 @@ class CrossAttention(nn.Module):
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
 
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-
-        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-
-        self.to_out = nn.Sequential(
-            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
-            nn.Dropout(dropout)
-        )
-
-    def forward(self, x, context=None, value=None, mask=None):
-        h = self.heads
-
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        if value is not None:
-            v = self.to_v(value)
-            del value
-        else:
-            v = self.to_v(context)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-        # force cast to fp32 to avoid overflowing
-        if _ATTN_PRECISION =="fp32":
-            with torch.autocast(enabled=False, device_type = 'cuda'):
-                q, k = q.float(), k.float()
-                sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-        else:
-            sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-        del q, k
-
-        if exists(mask):
-            mask = rearrange(mask, 'b ... -> b (...)')
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b j -> (b h) () j', h=h)
-            sim.masked_fill_(~mask, max_neg_value)
-
-        # attention, what we cannot get enough of
-        sim = sim.softmax(dim=-1)
-
-        out = einsum('b i j, b j d -> b i d', sim, v)
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-        return self.to_out(out)
-
-class MemoryEfficientCrossAttention(nn.Module):
-    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=ops):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
         self.heads = heads
         self.dim_head = dim_head
 
@@ -411,7 +324,6 @@ class MemoryEfficientCrossAttention(nn.Module):
         self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
 
         self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
-        self.attention_op: Optional[Any] = None
 
     def forward(self, x, context=None, value=None, mask=None):
         q = self.to_q(x)
@@ -423,85 +335,12 @@ class MemoryEfficientCrossAttention(nn.Module):
         else:
             v = self.to_v(context)
 
-        b, _, _ = q.shape
+        if mask is None:
-        q, k, v = map(
+            out = optimized_attention(q, k, v, self.heads)
-            lambda t: t.unsqueeze(3)
-            .reshape(b, t.shape[1], self.heads, self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * self.heads, t.shape[1], self.dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-        # actually compute the attention, what we cannot get enough of
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
-        if exists(mask):
-            raise NotImplementedError
-        out = (
-            out.unsqueeze(0)
-            .reshape(b, self.heads, out.shape[1], self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b, out.shape[1], self.heads * self.dim_head)
-        )
-        return self.to_out(out)
-
-class CrossAttentionPytorch(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops):
-        super().__init__()
-        inner_dim = dim_head * heads
-        context_dim = default(context_dim, query_dim)
-
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-
-        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x, context=None, value=None, mask=None):
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        if value is not None:
-            v = self.to_v(value)
-            del value
         else:
-            v = self.to_v(context)
+            out = optimized_attention_masked(q, k, v, self.heads, mask)
-
-        b, _, _ = q.shape
-        q, k, v = map(
-            lambda t: t.view(b, -1, self.heads, self.dim_head).transpose(1, 2),
-            (q, k, v),
-        )
-
-        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
-
-        if exists(mask):
-            raise NotImplementedError
-        out = (
-            out.transpose(1, 2).reshape(b, -1, self.heads * self.dim_head)
-        )
-
         return self.to_out(out)
 
-if model_management.xformers_enabled():
-    print("Using xformers cross attention")
-    CrossAttention = MemoryEfficientCrossAttention
-elif model_management.pytorch_attention_enabled():
-    print("Using pytorch cross attention")
-    CrossAttention = CrossAttentionPytorch
-else:
-    if args.use_split_cross_attention:
-        print("Using split optimization for cross attention")
-        CrossAttention = CrossAttentionDoggettx
-    else:
-        print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
-        CrossAttention = CrossAttentionBirchSan
-
 
 class BasicTransformerBlock(nn.Module):
     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,

comfy/ldm/modules/diffusionmodules/model.py

@@ -6,7 +6,6 @@ import numpy as np
 from einops import rearrange
 from typing import Optional, Any
 
-from ..attention import MemoryEfficientCrossAttention
 from .... import model_management
 from .... import ops
 
@@ -194,6 +193,52 @@ def slice_attention(q, k, v):
 
     return r1
 
+def normal_attention(q, k, v):
+    # compute attention
+    b,c,h,w = q.shape
+
+    q = q.reshape(b,c,h*w)
+    q = q.permute(0,2,1)   # b,hw,c
+    k = k.reshape(b,c,h*w) # b,c,hw
+    v = v.reshape(b,c,h*w)
+
+    r1 = slice_attention(q, k, v)
+    h_ = r1.reshape(b,c,h,w)
+    del r1
+    return h_
+
+def xformers_attention(q, k, v):
+    # compute attention
+    B, C, H, W = q.shape
+    q, k, v = map(
+        lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
+        (q, k, v),
+    )
+
+    try:
+        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
+        out = out.transpose(1, 2).reshape(B, C, H, W)
+    except NotImplementedError as e:
+        out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
+    return out
+
+def pytorch_attention(q, k, v):
+    # compute attention
+    B, C, H, W = q.shape
+    q, k, v = map(
+        lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
+        (q, k, v),
+    )
+
+    try:
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
+        out = out.transpose(2, 3).reshape(B, C, H, W)
+    except model_management.OOM_EXCEPTION as e:
+        print("scaled_dot_product_attention OOMed: switched to slice attention")
+        out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
+    return out
+
+
 class AttnBlock(nn.Module):
     def __init__(self, in_channels):
         super().__init__()
@@ -221,6 +266,16 @@ class AttnBlock(nn.Module):
                                         stride=1,
                                         padding=0)
 
+        if model_management.xformers_enabled_vae():
+            print("Using xformers attention in VAE")
+            self.optimized_attention = xformers_attention
+        elif model_management.pytorch_attention_enabled():
+            print("Using pytorch attention in VAE")
+            self.optimized_attention = pytorch_attention
+        else:
+            print("Using split attention in VAE")
+            self.optimized_attention = normal_attention
+
     def forward(self, x):
         h_ = x
         h_ = self.norm(h_)
@@ -228,161 +283,15 @@ class AttnBlock(nn.Module):
         k = self.k(h_)
         v = self.v(h_)
 
-        # compute attention
+        h_ = self.optimized_attention(q, k, v)
-        b,c,h,w = q.shape
 
-        q = q.reshape(b,c,h*w)
-        q = q.permute(0,2,1)   # b,hw,c
-        k = k.reshape(b,c,h*w) # b,c,hw
-        v = v.reshape(b,c,h*w)
-
-        r1 = slice_attention(q, k, v)
-        h_ = r1.reshape(b,c,h,w)
-        del r1
         h_ = self.proj_out(h_)
 
         return x+h_
 
-class MemoryEfficientAttnBlock(nn.Module):
-    """
-        Uses xformers efficient implementation,
-        see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-        Note: this is a single-head self-attention operation
-    """
-    #
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = ops.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        B, C, H, W = q.shape
-        q, k, v = map(
-            lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
-            (q, k, v),
-        )
-
-        try:
-            out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-            out = out.transpose(1, 2).reshape(B, C, H, W)
-        except NotImplementedError as e:
-            out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
-
-        out = self.proj_out(out)
-        return x+out
-
-class MemoryEfficientAttnBlockPytorch(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = ops.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = ops.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-        self.attention_op: Optional[Any] = None
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        B, C, H, W = q.shape
-        q, k, v = map(
-            lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
-            (q, k, v),
-        )
-
-        try:
-            out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
-            out = out.transpose(2, 3).reshape(B, C, H, W)
-        except model_management.OOM_EXCEPTION as e:
-            print("scaled_dot_product_attention OOMed: switched to slice attention")
-            out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
-
-        out = self.proj_out(out)
-        return x+out
-
-class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
-    def forward(self, x, context=None, mask=None):
-        b, c, h, w = x.shape
-        x = rearrange(x, 'b c h w -> b (h w) c')
-        out = super().forward(x, context=context, mask=mask)
-        out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
-        return x + out
-
 
 def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
-    assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
+    return AttnBlock(in_channels)
-    if model_management.xformers_enabled_vae() and attn_type == "vanilla":
-        attn_type = "vanilla-xformers"
-    if model_management.pytorch_attention_enabled() and attn_type == "vanilla":
-        attn_type = "vanilla-pytorch"
-    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
-    if attn_type == "vanilla":
-        assert attn_kwargs is None
-        return AttnBlock(in_channels)
-    elif attn_type == "vanilla-xformers":
-        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
-        return MemoryEfficientAttnBlock(in_channels)
-    elif attn_type == "vanilla-pytorch":
-        return MemoryEfficientAttnBlockPytorch(in_channels)
-    elif type == "memory-efficient-cross-attn":
-        attn_kwargs["query_dim"] = in_channels
-        return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
-    elif attn_type == "none":
-        return nn.Identity(in_channels)
-    else:
-        raise NotImplementedError()
 
 
 class Model(nn.Module):

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -296,8 +296,7 @@ class UNetModel(nn.Module):
         dims=2,
         num_classes=None,
         use_checkpoint=False,
-        use_fp16=False,
+        dtype=th.float32,
-        use_bf16=False,
         num_heads=-1,
         num_head_channels=-1,
         num_heads_upsample=-1,
@@ -370,8 +369,7 @@ class UNetModel(nn.Module):
         self.conv_resample = conv_resample
         self.num_classes = num_classes
         self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
+        self.dtype = dtype
-        self.dtype = th.bfloat16 if use_bf16 else self.dtype
         self.num_heads = num_heads
         self.num_head_channels = num_head_channels
         self.num_heads_upsample = num_heads_upsample

comfy/model_detection.py

@@ -14,7 +14,7 @@ def count_blocks(state_dict_keys, prefix_string):
         count += 1
     return count
 
-def detect_unet_config(state_dict, key_prefix, use_fp16):
+def detect_unet_config(state_dict, key_prefix, dtype):
     state_dict_keys = list(state_dict.keys())
 
     unet_config = {
@@ -32,7 +32,7 @@ def detect_unet_config(state_dict, key_prefix, use_fp16):
     else:
         unet_config["adm_in_channels"] = None
 
-    unet_config["use_fp16"] = use_fp16
+    unet_config["dtype"] = dtype
     model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
     in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
 
@@ -116,15 +116,15 @@ def model_config_from_unet_config(unet_config):
     print("no match", unet_config)
     return None
 
-def model_config_from_unet(state_dict, unet_key_prefix, use_fp16, use_base_if_no_match=False):
+def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False):
-    unet_config = detect_unet_config(state_dict, unet_key_prefix, use_fp16)
+    unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype)
     model_config = model_config_from_unet_config(unet_config)
     if model_config is None and use_base_if_no_match:
         return comfy.supported_models_base.BASE(unet_config)
     else:
         return model_config
 
-def unet_config_from_diffusers_unet(state_dict, use_fp16):
+def unet_config_from_diffusers_unet(state_dict, dtype):
     match = {}
     attention_resolutions = []
 
@@ -147,47 +147,47 @@ def unet_config_from_diffusers_unet(state_dict, use_fp16):
         match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
 
     SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
+            'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
             'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4],
             'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
 
     SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                    'num_classes': 'sequential', 'adm_in_channels': 2560, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 384,
+                    'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
                     'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 4, 4, 0], 'channel_mult': [1, 2, 4, 4],
                     'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, "num_head_channels": 64}
 
     SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
+            'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
             'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
             'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64}
 
     SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                    'num_classes': 'sequential', 'adm_in_channels': 2048, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
+                    'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
                     'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
                     'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64}
 
     SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-                    'num_classes': 'sequential', 'adm_in_channels': 1536, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
+                    'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
                     'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
                     'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024}
 
     SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
+            'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
             'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
             'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, "num_heads": 8}
 
     SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
+            'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
             'num_res_blocks': 2, 'attention_resolutions': [4], 'transformer_depth': [0, 0, 1], 'channel_mult': [1, 2, 4],
             'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
 
     SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
+            'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
             'num_res_blocks': 2, 'attention_resolutions': [], 'transformer_depth': [0, 0, 0], 'channel_mult': [1, 2, 4],
             'transformer_depth_middle': 0, 'use_linear_in_transformer': True, "num_head_channels": 64, 'context_dim': 1}
 
     SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
-            'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 9, 'model_channels': 320,
+            'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
             'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4],
             'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
 
@@ -203,8 +203,8 @@ def unet_config_from_diffusers_unet(state_dict, use_fp16):
             return unet_config
     return None
 
-def model_config_from_diffusers_unet(state_dict, use_fp16):
+def model_config_from_diffusers_unet(state_dict, dtype):
-    unet_config = unet_config_from_diffusers_unet(state_dict, use_fp16)
+    unet_config = unet_config_from_diffusers_unet(state_dict, dtype)
     if unet_config is not None:
         return model_config_from_unet_config(unet_config)
     return None

comfy/model_management.py

@@ -154,14 +154,18 @@ def is_nvidia():
             return True
     return False
 
-ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention
+ENABLE_PYTORCH_ATTENTION = False
+if args.use_pytorch_cross_attention:
+    ENABLE_PYTORCH_ATTENTION = True
+    XFORMERS_IS_AVAILABLE = False
+
 VAE_DTYPE = torch.float32
 
 try:
     if is_nvidia():
         torch_version = torch.version.__version__
         if int(torch_version[0]) >= 2:
-            if ENABLE_PYTORCH_ATTENTION == False and XFORMERS_IS_AVAILABLE == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
+            if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                 ENABLE_PYTORCH_ATTENTION = True
             if torch.cuda.is_bf16_supported():
                 VAE_DTYPE = torch.bfloat16
@@ -186,7 +190,6 @@ if ENABLE_PYTORCH_ATTENTION:
     torch.backends.cuda.enable_math_sdp(True)
     torch.backends.cuda.enable_flash_sdp(True)
     torch.backends.cuda.enable_mem_efficient_sdp(True)
-    XFORMERS_IS_AVAILABLE = False
 
 if args.lowvram:
     set_vram_to = VRAMState.LOW_VRAM
@@ -354,6 +357,8 @@ def load_models_gpu(models, memory_required=0):
             current_loaded_models.insert(0, current_loaded_models.pop(index))
             models_already_loaded.append(loaded_model)
         else:
+            if hasattr(x, "model"):
+                print(f"Requested to load {x.model.__class__.__name__}")
             models_to_load.append(loaded_model)
 
     if len(models_to_load) == 0:
@@ -363,7 +368,7 @@ def load_models_gpu(models, memory_required=0):
                 free_memory(extra_mem, d, models_already_loaded)
         return
 
-    print("loading new")
+    print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
 
     total_memory_required = {}
     for loaded_model in models_to_load:
@@ -405,7 +410,6 @@ def load_model_gpu(model):
 def cleanup_models():
     to_delete = []
     for i in range(len(current_loaded_models)):
-        print(sys.getrefcount(current_loaded_models[i].model))
         if sys.getrefcount(current_loaded_models[i].model) <= 2:
             to_delete = [i] + to_delete
 
@@ -444,6 +448,13 @@ def unet_inital_load_device(parameters, dtype):
     else:
         return cpu_dev
 
+def unet_dtype(device=None, model_params=0):
+    if args.bf16_unet:
+        return torch.bfloat16
+    if should_use_fp16(device=device, model_params=model_params):
+        return torch.float16
+    return torch.float32
+
 def text_encoder_offload_device():
     if args.gpu_only:
         return get_torch_device()
@@ -656,7 +667,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
         return False
 
     #FP16 is just broken on these cards
-    nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX"]
+    nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"]
     for x in nvidia_16_series:
         if x in props.name:
             return False

comfy/model_patcher.py

@@ -107,6 +107,10 @@ class ModelPatcher:
                 for k in patch_list:
                     if hasattr(patch_list[k], "to"):
                         patch_list[k] = patch_list[k].to(device)
+        if "unet_wrapper_function" in self.model_options:
+            wrap_func = self.model_options["unet_wrapper_function"]
+            if hasattr(wrap_func, "to"):
+                self.model_options["unet_wrapper_function"] = wrap_func.to(device)
 
     def model_dtype(self):
         if hasattr(self.model, "get_dtype"):

comfy/nodes/base_nodes.py

@@ -1185,7 +1185,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
         noise_mask = latent["noise_mask"]
 
     callback = latent_preview.prepare_callback(model, steps)
-    disable_pbar = False
+    disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
     samples = sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
                                   denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
                                   force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
@@ -1645,7 +1645,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "KSampler": "KSampler",
     "KSamplerAdvanced": "KSampler (Advanced)",
     # Loaders
-    "CheckpointLoader": "Load Checkpoint (With Config)",
+    "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)",
     "CheckpointLoaderSimple": "Load Checkpoint",
     "VAELoader": "Load VAE",
     "LoraLoader": "Load LoRA",

comfy/sample.py

@@ -29,7 +29,7 @@ def prepare_mask(noise_mask, shape, device):
     noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
     noise_mask = noise_mask.round()
     noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
-    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
+    noise_mask = utils.repeat_to_batch_size(noise_mask, shape[0])
     noise_mask = noise_mask.to(device)
     return noise_mask
 

comfy/sd.py

@@ -327,7 +327,9 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
         if "params" in model_config_params["unet_config"]:
             unet_config = model_config_params["unet_config"]["params"]
             if "use_fp16" in unet_config:
-                fp16 = unet_config["use_fp16"]
+                fp16 = unet_config.pop("use_fp16")
+                if fp16:
+                    unet_config["dtype"] = torch.float16
 
     noise_aug_config = None
     if "noise_aug_config" in model_config_params:
@@ -405,12 +407,12 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     clip_target = None
 
     parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.")
-    fp16 = model_management.should_use_fp16(model_params=parameters)
+    unet_dtype = model_management.unet_dtype(model_params=parameters)
 
     class WeightsLoader(torch.nn.Module):
         pass
 
-    model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", fp16)
+    model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype)
     if model_config is None:
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
 
@@ -418,12 +420,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         if output_clipvision:
             clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
 
-    dtype = torch.float32
-    if fp16:
-        dtype = torch.float16
-
     if output_model:
-        inital_load_device = model_management.unet_inital_load_device(parameters, dtype)
+        inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
         offload_device = model_management.unet_offload_device()
         model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device)
         model.load_model_weights(sd, "model.diffusion_model.")
@@ -458,15 +456,15 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
 def load_unet(unet_path): #load unet in diffusers format
     sd = comfy.utils.load_torch_file(unet_path)
     parameters = comfy.utils.calculate_parameters(sd)
-    fp16 = model_management.should_use_fp16(model_params=parameters)
+    unet_dtype = model_management.unet_dtype(model_params=parameters)
     if "input_blocks.0.0.weight" in sd: #ldm
-        model_config = model_detection.model_config_from_unet(sd, "", fp16)
+        model_config = model_detection.model_config_from_unet(sd, "", unet_dtype)
         if model_config is None:
             raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
         new_sd = sd
 
     else: #diffusers
-        model_config = model_detection.model_config_from_diffusers_unet(sd, fp16)
+        model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype)
         if model_config is None:
             print("ERROR UNSUPPORTED UNET", unet_path)
             return None

comfy/taesd/taesd.py

@@ -6,6 +6,8 @@ Tiny AutoEncoder for Stable Diffusion
 import torch
 import torch.nn as nn
 
+import comfy.utils
+
 def conv(n_in, n_out, **kwargs):
     return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
 
@@ -50,9 +52,9 @@ class TAESD(nn.Module):
         self.encoder = Encoder()
         self.decoder = Decoder()
         if encoder_path is not None:
-            self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu", weights_only=True))
+            self.encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
         if decoder_path is not None:
-            self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu", weights_only=True))
+            self.decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True))
 
     @staticmethod
     def scale_latents(x):

comfy/utils.py

@@ -410,6 +410,10 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
         output[b:b+1] = out/out_div
     return output
 
+PROGRESS_BAR_ENABLED = True
+def set_progress_bar_enabled(enabled):
+    global PROGRESS_BAR_ENABLED
+    PROGRESS_BAR_ENABLED = enabled
 
 PROGRESS_BAR_HOOK = None
 def set_progress_bar_global_hook(function):

comfy_extras/nodes/nodes_compositing.py

@@ -158,7 +158,7 @@ class SplitImageWithAlpha:
     def split_image_with_alpha(self, image: torch.Tensor):
         out_images = [i[:,:,:3] for i in image]
         out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image]
-        result = (torch.stack(out_images), torch.stack(out_alphas))
+        result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas))
         return result
 
 
@@ -180,7 +180,7 @@ class JoinImageWithAlpha:
         batch_size = min(len(image), len(alpha))
         out_images = []
 
-        alpha = resize_mask(alpha, image.shape[1:])
+        alpha = 1.0 - resize_mask(alpha, image.shape[1:])
         for i in range(batch_size):
            out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
 

comfy_extras/nodes/nodes_custom_sampler.py

@@ -3,6 +3,7 @@ import comfy.sample
 from comfy.k_diffusion import sampling as k_diffusion_sampling
 import latent_preview
 import torch
+import comfy.utils
 
 
 class BasicScheduler:
@@ -15,7 +16,7 @@ class BasicScheduler:
                       }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sigmas"
 
@@ -35,7 +36,7 @@ class KarrasScheduler:
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sigmas"
 
@@ -53,7 +54,7 @@ class ExponentialScheduler:
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sigmas"
 
@@ -72,7 +73,7 @@ class PolyexponentialScheduler:
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sigmas"
 
@@ -91,7 +92,7 @@ class VPScheduler:
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sigmas"
 
@@ -108,7 +109,7 @@ class SplitSigmas:
                      }
                 }
     RETURN_TYPES = ("SIGMAS","SIGMAS")
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sigmas"
 
@@ -125,7 +126,7 @@ class KSamplerSelect:
                       }
                }
     RETURN_TYPES = ("SAMPLER",)
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sampler"
 
@@ -144,7 +145,7 @@ class SamplerDPMPP_2M_SDE:
                       }
                }
     RETURN_TYPES = ("SAMPLER",)
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sampler"
 
@@ -168,7 +169,7 @@ class SamplerDPMPP_SDE:
                       }
                }
     RETURN_TYPES = ("SAMPLER",)
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     FUNCTION = "get_sampler"
 
@@ -201,7 +202,7 @@ class SamplerCustom:
 
     FUNCTION = "sample"
 
-    CATEGORY = "_for_testing/custom_sampling"
+    CATEGORY = "sampling/custom_sampling"
 
     def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image):
         latent = latent_image
@@ -219,7 +220,7 @@ class SamplerCustom:
         x0_output = {}
         callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
 
-        disable_pbar = False
+        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
         samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
 
         out = latent.copy()

comfy_extras/nodes/nodes_mask.py

@@ -241,8 +241,8 @@ class MaskComposite:
         right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
         visible_width, visible_height = (right - left, bottom - top,)
 
-        source_portion = source[:visible_height, :visible_width]
+        source_portion = source[:, :visible_height, :visible_width]
-        destination_portion = destination[top:bottom, left:right]
+        destination_portion = destination[:, top:bottom, left:right]
 
         if operation == "multiply":
             output[:, top:bottom, left:right] = destination_portion * source_portion
@@ -283,10 +283,10 @@ class FeatherMask:
     def feather(self, mask, left, top, right, bottom):
         output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()
 
-        left = min(left, output.shape[1])
+        left = min(left, output.shape[-1])
-        right = min(right, output.shape[1])
+        right = min(right, output.shape[-1])
-        top = min(top, output.shape[0])
+        top = min(top, output.shape[-2])
-        bottom = min(bottom, output.shape[0])
+        bottom = min(bottom, output.shape[-2])
 
         for x in range(left):
             feather_rate = (x + 1.0) / left

comfy_extras/nodes/nodes_model_merging.py

@@ -1,5 +1,6 @@
 from comfy import sd
 from comfy import model_base
+import comfy.model_management
 
 from comfy.cmd import folder_paths
 import json
@@ -177,6 +178,95 @@ class CheckpointSave:
         sd.save_checkpoint(output_checkpoint, model, clip, vae, metadata=metadata)
         return {}
 
+class CLIPSave:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip": ("CLIP",),
+                              "filename_prefix": ("STRING", {"default": "clip/ComfyUI"}),},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "advanced/model_merging"
+
+    def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None):
+        prompt_info = ""
+        if prompt is not None:
+            prompt_info = json.dumps(prompt)
+
+        metadata = {}
+        if not args.disable_metadata:
+            metadata["prompt"] = prompt_info
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata[x] = json.dumps(extra_pnginfo[x])
+
+        comfy.model_management.load_models_gpu([clip.load_model()])
+        clip_sd = clip.get_sd()
+
+        for prefix in ["clip_l.", "clip_g.", ""]:
+            k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys()))
+            current_clip_sd = {}
+            for x in k:
+                current_clip_sd[x] = clip_sd.pop(x)
+            if len(current_clip_sd) == 0:
+                continue
+
+            p = prefix[:-1]
+            replace_prefix = {}
+            filename_prefix_ = filename_prefix
+            if len(p) > 0:
+                filename_prefix_ = "{}_{}".format(filename_prefix_, p)
+                replace_prefix[prefix] = ""
+            replace_prefix["transformer."] = ""
+
+            full_output_folder, filename, counter, subfolder, filename_prefix_ = folder_paths.get_save_image_path(filename_prefix_, self.output_dir)
+
+            output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+            output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+            current_clip_sd = comfy.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix)
+
+            comfy.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata)
+        return {}
+
+class VAESave:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "vae": ("VAE",),
+                              "filename_prefix": ("STRING", {"default": "vae/ComfyUI_vae"}),},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "advanced/model_merging"
+
+    def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        prompt_info = ""
+        if prompt is not None:
+            prompt_info = json.dumps(prompt)
+
+        metadata = {}
+        if not args.disable_metadata:
+            metadata["prompt"] = prompt_info
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata[x] = json.dumps(extra_pnginfo[x])
+
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        comfy.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata)
+        return {}
 
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSimple": ModelMergeSimple,
@@ -185,4 +275,6 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeAdd": ModelAdd,
     "CheckpointSave": CheckpointSave,
     "CLIPMergeSimple": CLIPMergeSimple,
+    "CLIPSave": CLIPSave,
+    "VAESave": VAESave,
 }

extra_model_paths.yaml.example

@@ -1,5 +1,6 @@
 #Rename this to extra_model_paths.yaml and ComfyUI will load it
 
+
 #config for a1111 ui
 #all you have to do is change the base_path to where yours is installed
 a111:
@@ -19,6 +20,21 @@ a111:
     hypernetworks: models/hypernetworks
     controlnet: models/ControlNet
 
+#config for comfyui
+#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc.
+
+#comfyui:
+#     base_path: path/to/comfyui/
+#     checkpoints: models/checkpoints/
+#     clip: models/clip/
+#     clip_vision: models/clip_vision/
+#     configs: models/configs/
+#     controlnet: models/controlnet/
+#     embeddings: models/embeddings/
+#     loras: models/loras/
+#     upscale_models: models/upscale_models/
+#     vae: models/vae/
+
 #other_ui:
 #    base_path: path/to/ui
 #    checkpoints: models/checkpoints

mypy.ini

@@ -0,0 +1,4 @@
+[mypy]
+files = comfy/, comfy_extras/
+ignore_missing_imports = True
+strict_optional = True

requirements.txt

@@ -26,4 +26,5 @@ Pillow
 scipy
 tqdm
 protobuf==3.20.3
-psutil
+psutil
+mypy>=1.6.0

setup.py

@@ -4,6 +4,7 @@ import os.path
 import platform
 import subprocess
 import sys
+from typing import List
 
 from pip._internal.index.collector import LinkCollector
 from pip._internal.index.package_finder import PackageFinder
@@ -106,7 +107,7 @@ def _is_linux_arm64():
     return os_name == 'Linux' and architecture == 'aarch64'
 
 
-def dependencies() -> [str]:
+def dependencies() -> List[str]:
     _dependencies = open(os.path.join(os.path.dirname(__file__), "requirements.txt")).readlines()
     # todo: also add all plugin dependencies
     _alternative_indices = [amd_torch_index, nvidia_torch_index, cpu_torch_index_nightlies]
@@ -137,7 +138,7 @@ def dependencies() -> [str]:
     except:
         try:
             # pip 22
-            finder = PackageFinder.create(LinkCollector(session, SearchScope([], index_urls)),
+            finder = PackageFinder.create(LinkCollector(session, SearchScope([], index_urls)),  # type: ignore
                                           SelectionPreferences(allow_yanked=False, prefer_binary=False,
                                                                allow_all_prereleases=True)
                                           , use_deprecated_html5lib=False)

web/extensions/core/groupOptions.js

@@ -5,6 +5,61 @@ function setNodeMode(node, mode) {
     node.graph.change();
 }
 
+function addNodesToGroup(group, nodes=[]) {
+    var x1, y1, x2, y2;
+    var nx1, ny1, nx2, ny2;
+    var node;
+
+    x1 = y1 = x2 = y2 = -1;
+    nx1 = ny1 = nx2 = ny2 = -1;
+
+    for (var n of [group._nodes, nodes]) {
+        for (var i in n) {
+            node = n[i]
+
+            nx1 = node.pos[0]
+            ny1 = node.pos[1]
+            nx2 = node.pos[0] + node.size[0]
+            ny2 = node.pos[1] + node.size[1]
+
+            if (node.type != "Reroute") {
+                ny1 -= LiteGraph.NODE_TITLE_HEIGHT;
+            }
+
+            if (node.flags?.collapsed) {
+                ny2 = ny1 + LiteGraph.NODE_TITLE_HEIGHT;
+
+                if (node?._collapsed_width) {
+                    nx2 = nx1 + Math.round(node._collapsed_width);
+                }
+            }
+
+            if (x1 == -1 || nx1 < x1) {
+                x1 = nx1;
+            }
+
+            if (y1 == -1 || ny1 < y1) {
+                y1 = ny1;
+            }
+
+            if (x2 == -1 || nx2 > x2) {
+                x2 = nx2;
+            }
+
+            if (y2 == -1 || ny2 > y2) {
+                y2 = ny2;
+            }
+        }
+    }
+
+    var padding = 10;
+
+    y1 = y1 - Math.round(group.font_size * 1.4);
+
+    group.pos = [x1 - padding, y1 - padding];
+    group.size = [x2 - x1 + padding * 2, y2 - y1 + padding * 2];
+}
+
 app.registerExtension({
     name: "Comfy.GroupOptions",
     setup() {
@@ -14,6 +69,17 @@ app.registerExtension({
             const options = orig.apply(this, arguments);
             const group = this.graph.getGroupOnPos(this.graph_mouse[0], this.graph_mouse[1]);
             if (!group) {
+                options.push({
+                    content: "Add Group For Selected Nodes",
+                    disabled: !Object.keys(app.canvas.selected_nodes || {}).length,
+                    callback: () => {
+                        var group = new LiteGraph.LGraphGroup();
+                        addNodesToGroup(group, this.selected_nodes)
+                        app.canvas.graph.add(group);
+                        this.graph.change();
+                    }
+                });
+
                 return options;
             }
 
@@ -21,6 +87,15 @@ app.registerExtension({
             group.recomputeInsideNodes();
             const nodesInGroup = group._nodes;
 
+            options.push({
+                content: "Add Selected Nodes To Group",
+                disabled: !Object.keys(app.canvas.selected_nodes || {}).length,
+                callback: () => {
+                    addNodesToGroup(group, this.selected_nodes)
+                    this.graph.change();
+                }
+            });
+
             // No nodes in group, return default options
             if (nodesInGroup.length === 0) {
                 return options;
@@ -38,6 +113,23 @@ app.registerExtension({
                 }
             }
 
+            options.push({
+                content: "Fit Group To Nodes",
+                callback: () => {
+                    addNodesToGroup(group)
+                    this.graph.change();
+                }
+            });
+
+            options.push({
+                content: "Select Nodes",
+                callback: () => {
+                    this.selectNodes(nodesInGroup);
+                    this.graph.change();
+                    this.canvas.focus();
+                }
+            });
+
             // Modes
             // 0: Always
             // 1: On Event

web/extensions/core/widgetInputs.js

@@ -200,6 +200,10 @@ app.registerExtension({
 				for (const input of this.inputs) {
 					if (input.widget && !input.widget[GET_CONFIG]) {
 						input.widget[GET_CONFIG] = () => getConfig.call(this, input.widget.name);
+						const w = this.widgets.find((w) => w.name === input.widget.name);
+						if (w) {
+							hideWidget(this, w);
+						}
 					}
 				}
 			}

web/lib/litegraph.core.js

@@ -3796,7 +3796,7 @@
         out = out || new Float32Array(4);
         out[0] = this.pos[0] - 4;
         out[1] = this.pos[1] - LiteGraph.NODE_TITLE_HEIGHT;
-        out[2] = this.size[0] + 4;
+        out[2] = this.flags.collapsed ? (this._collapsed_width || LiteGraph.NODE_COLLAPSED_WIDTH) : this.size[0] + 4;
         out[3] = this.flags.collapsed ? LiteGraph.NODE_TITLE_HEIGHT : this.size[1] + LiteGraph.NODE_TITLE_HEIGHT;
 
         if (this.onBounding) {

web/scripts/app.js

@@ -450,6 +450,47 @@ export class ComfyApp {
 					}
 				}
 
+				function calculateGrid(w, h, n) {
+					let columns, rows, cellsize;
+
+					if (w > h) {
+						cellsize = h;
+						columns = Math.ceil(w / cellsize);
+						rows = Math.ceil(n / columns);
+					} else {
+						cellsize = w;
+						rows = Math.ceil(h / cellsize);
+						columns = Math.ceil(n / rows);
+					}
+
+					while (columns * rows < n) {
+						cellsize++;
+						if (w >= h) {
+							columns = Math.ceil(w / cellsize);
+							rows = Math.ceil(n / columns);
+						} else {
+							rows = Math.ceil(h / cellsize);
+							columns = Math.ceil(n / rows);
+						}
+					}
+
+					const cell_size = Math.min(w/columns, h/rows);
+					return {cell_size, columns, rows};
+				}
+
+				function is_all_same_aspect_ratio(imgs) {
+					// assume: imgs.length >= 2
+					let ratio = imgs[0].naturalWidth/imgs[0].naturalHeight;
+
+					for(let i=1; i<imgs.length; i++) {
+						let this_ratio = imgs[i].naturalWidth/imgs[i].naturalHeight;
+						if(ratio != this_ratio)
+							return false;
+					}
+
+					return true;
+				}
+
 				if (this.imgs && this.imgs.length) {
 					const canvas = graph.list_of_graphcanvas[0];
 					const mouse = canvas.graph_mouse;
@@ -460,44 +501,60 @@ export class ComfyApp {
 						this.pointerDown = null;
 					}
 
-					let w = this.imgs[0].naturalWidth;
-					let h = this.imgs[0].naturalHeight;
 					let imageIndex = this.imageIndex;
 					const numImages = this.imgs.length;
 					if (numImages === 1 && !imageIndex) {
 						this.imageIndex = imageIndex = 0;
 					}
 
-					const shiftY = getImageTop(this);
+					const top = getImageTop(this);
+					var shiftY = top;
 
 					let dw = this.size[0];
 					let dh = this.size[1];
 					dh -= shiftY;
 
 					if (imageIndex == null) {
-						let best = 0;
+						var cellWidth, cellHeight, shiftX, cell_padding, cols;
-						let cellWidth;
-						let cellHeight;
-						let cols = 0;
-						let shiftX = 0;
-						for (let c = 1; c <= numImages; c++) {
-							const rows = Math.ceil(numImages / c);
-							const cW = dw / c;
-							const cH = dh / rows;
-							const scaleX = cW / w;
-							const scaleY = cH / h;
 
-							const scale = Math.min(scaleX, scaleY, 1);
+						const compact_mode = is_all_same_aspect_ratio(this.imgs);
-							const imageW = w * scale;
+						if(!compact_mode) {
-							const imageH = h * scale;
+							// use rectangle cell style and border line
-							const area = imageW * imageH * numImages;
+							cell_padding = 2;
+							const { cell_size, columns, rows } = calculateGrid(dw, dh, numImages);
+							cols = columns;
 
-							if (area > best) {
+							cellWidth = cell_size;
-								best = area;
+							cellHeight = cell_size;
-								cellWidth = imageW;
+							shiftX = (dw-cell_size*cols)/2;
-								cellHeight = imageH;
+							shiftY = (dh-cell_size*rows)/2 + top;
-								cols = c;
+						}
-								shiftX = c * ((cW - imageW) / 2);
+						else {
+							cell_padding = 0;
+							let best = 0;
+							let w = this.imgs[0].naturalWidth;
+							let h = this.imgs[0].naturalHeight;
+
+							// compact style
+							for (let c = 1; c <= numImages; c++) {
+								const rows = Math.ceil(numImages / c);
+								const cW = dw / c;
+								const cH = dh / rows;
+								const scaleX = cW / w;
+								const scaleY = cH / h;
+
+								const scale = Math.min(scaleX, scaleY, 1);
+								const imageW = w * scale;
+								const imageH = h * scale;
+								const area = imageW * imageH * numImages;
+
+								if (area > best) {
+									best = area;
+									cellWidth = imageW;
+									cellHeight = imageH;
+									cols = c;
+									shiftX = c * ((cW - imageW) / 2);
+								}
 							}
 						}
 
@@ -542,7 +599,14 @@ export class ComfyApp {
 							let imgWidth = ratio * img.width;
 							let imgX = col * cellWidth + shiftX + (cellWidth - imgWidth)/2;
 
-							ctx.drawImage(img, imgX, imgY, imgWidth, imgHeight);
+							ctx.drawImage(img, imgX+cell_padding, imgY+cell_padding, imgWidth-cell_padding*2, imgHeight-cell_padding*2);
+							if(!compact_mode) {
+								// rectangle cell and border line style
+								ctx.strokeStyle = "#8F8F8F";
+								ctx.lineWidth = 1;
+								ctx.strokeRect(x+cell_padding, y+cell_padding, cellWidth-cell_padding*2, cellHeight-cell_padding*2);
+							}
+
 							ctx.filter = "none";
 						}
 
@@ -552,6 +616,9 @@ export class ComfyApp {
 						}
 					} else {
 						// Draw individual
+						let w = this.imgs[imageIndex].naturalWidth;
+						let h = this.imgs[imageIndex].naturalHeight;
+
 						const scaleX = dw / w;
 						const scaleY = dh / h;
 						const scale = Math.min(scaleX, scaleY, 1);
@@ -594,14 +661,14 @@ export class ComfyApp {
 						};
 
 						if (numImages > 1) {
-							if (drawButton(x + w - 35, y + h - 35, 30, `${this.imageIndex + 1}/${numImages}`)) {
+							if (drawButton(dw - 40, dh + top - 40, 30, `${this.imageIndex + 1}/${numImages}`)) {
 								let i = this.imageIndex + 1 >= numImages ? 0 : this.imageIndex + 1;
 								if (!this.pointerDown || !this.pointerDown.index === i) {
 									this.pointerDown = { index: i, pos: [...mouse] };
 								}
 							}
 
-							if (drawButton(x + w - 35, y + 5, 30, `x`)) {
+							if (drawButton(dw - 40, top + 10, 30, `x`)) {
 								if (!this.pointerDown || !this.pointerDown.index === null) {
 									this.pointerDown = { index: null, pos: [...mouse] };
 								}
@@ -861,6 +928,16 @@ export class ComfyApp {
 					block_default = true;
 				}
 
+				// Alt + C collapse/uncollapse
+				if (e.key === 'c' && e.altKey) {
+					if (this.selected_nodes) {
+						for (var i in this.selected_nodes) {
+							this.selected_nodes[i].collapse()
+						}
+					}
+					block_default = true;
+				}
+
 				// Ctrl+C Copy
 				if ((e.key === 'c') && (e.metaKey || e.ctrlKey)) {
 					// Trigger onCopy
@@ -1525,7 +1602,7 @@ export class ComfyApp {
 								all_inputs = all_inputs.concat(Object.keys(parent.inputs))
 								for (let parent_input in all_inputs) {
 									parent_input = all_inputs[parent_input];
-									if (parent.inputs[parent_input].type === node.inputs[i].type) {
+									if (parent.inputs[parent_input]?.type === node.inputs[i].type) {
 										link = parent.getInputLink(parent_input);
 										if (link) {
 											parent = parent.getInputNode(parent_input);

web/scripts/ui.js

@@ -809,7 +809,8 @@ export class ComfyUI {
 			if (
 				this.lastQueueSize != 0 &&
 				status.exec_info.queue_remaining == 0 &&
-				document.getElementById("autoQueueCheckbox").checked
+				document.getElementById("autoQueueCheckbox").checked &&
+				! app.lastExecutionError
 			) {
 				app.queuePrompt(0, this.batchCount);
 			}